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Understanding of its correlation to some relevant factors is of paramount importance for modeling and predication of the air pollution process. Compared with the traditional cross-correlation analysis, multifractal detrended cross-correlation analysis (MFDCCA) was argued to be a more suitable method to analyze air pollutant time series due to their non-stationarity nature. Multifractal temporally weighted detrended cross-correlation analysis (MF-TWXDFA) was proposed to improve the shortcomings of MFDCCA. In this study, we apply MF-TWXDFA to investigate the cross-correlation between pollutants ( PM 1 0 and NO X ) and meteorological factors (temperature, pressure, wind speed (WS) and relative humidity (RH)). The results on the dataset from 1 January 2005 to 31 December 2014 in urban and rural areas of Hong Kong show the existence of multifractal cross-correlation between all pairs of pollutants and meteorological factors in both urban and rural areas. Different from the previous MFDCCA results, we found that the multifractal degree of cross-correlation between PM 1 0 and (temperature, pressure) is more obvious in urban area. The multifractal strength of cross-correlation between NO X and WS is very weak in either urban or rural area. Furthermore, the MF-TWXDFA cross-correlation coefficient ρ MF-TWXDFA can capture negative correlation between pollutants and meteorological factors. For PM 1 0 , ρ MF-TWXDFA in urban area is less than or close to that in rural area with respect to these four meteorological factors. The ρ MF-TWXDFA of NO X in urban and rural areas shows more complex patterns for varied meteorological factors. Compared with MFDCCA, MF-TWXDFA can provide much richer information about the relationships between pollutants and meteorological factors, which is beneficial to further modeling and prediction of the air pollution process. [ABSTRACT FROM AUTHOR]

Air quality forecasting for Hong Kong is a challenge. Even taking the advantages of auto-regressive integrated moving average and some state-of-the-art numerical models, a recently-developed hybrid method for one-day (two- and three-day) ahead forecasting performs similarly to (slightly better than) a simple persistence forecasting. Long-term forecasting also remains an important issue, especially for policy decision for better control of air pollution and for evaluation of the long-term impacts on public health. Given the well-recognized negative effects of PM 2.5 , NO 2 and O 3 on public health, we study their time series under the multi-scale framework with empirical mode decomposition and nonstationary oscillation resampling to explore the possibility of long-term forecasting and to improve short-term forecasts in Hong Kong. Applied to a dataset from January 2016 to December 2018, the long-term forecasting (with lead time about 100 days) of the multi-scale framework has the root-mean-square error (RMSE) comparable with that of the short-term (with lead time of one or two days) forecasting by the persistence method, while its improvement for short-term forecasting (with lead time of one, two or three days) is quite substantial over the persistence forecasting, with RMSEs reduced by respectively 44%–47%, 30%–45%, and 40%–60% for PM 2.5 , NO 2 , and O 3. Compared to the hybrid method, it turns out that, for short-term forecasting for the same data, the multi-scale framework can reduce RMSE by about 25% (respectively 30%) for PM 2.5 (respectively NO 2 and O 3). In addition, we find no significant difference in the forecasting performance of the multi-scale framework among different types of stations. The multi-scale framework is feasible for time series forecasting and applicable to other pollutants in other cities. Image 1 • A multi-scale framework is applied to forecast PM 2.5 , NO 2 and O 3. • It uses empirical mode decomposition to extract relatively regular components. • It uses nonstationary oscillation resampling to forecast each component. • It satisfactorily forecasts long-term (about 100 days) variation of air pollutants. • It improves forecasting of air pollutants on the next 1–3 days. Use a multi-scale framework with empirical mode decomposition and nonstationary oscillation resampling to satisfactorily predict long-term variation and to improve short-term forecasts of air quality. [ABSTRACT FROM AUTHOR]